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Open Access


Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

ORCID logo

  • Junyi Zhang, 
  • Yigang Ding, 
  • Xinru Yang, 
  • Jinping Zhong, 
  • XinXin Qiu, 
  • Zhishan Zou, 
  • Yujie Xu, 
  • Xiunan Jin, 
  • Xiaomin Wu, 


  • Published: August 23, 2022
  • Reader Comments

Table 1

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016.

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from and are now shared fully on Github ( ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

  • (1) Can the COVID-19 pandemic promote the expansion of online learning?
  • (2a) What are the characteristics of online learning interaction during the pandemic?
  • (2b) What are the characteristics of online learning interaction after the pandemic?
  • (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.


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To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).


3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .


To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

  • (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
  • (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
  • (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
  • (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
  • (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
  • (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .


To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .


3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.


In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .


From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.


Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.


4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.


Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.


Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.


In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .


As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.


(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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Academic performance under COVID-19: The role of online learning readiness and emotional competence

1 University of Alabama, Tuscaloosa, AL 35487 USA

Wenjing Guo

2 Beijing Normal University, Beijing, China

3 Dalian Neusoft University of Information, Dalian, China

Associated Data

The authors do not have permission to share the data used in this study.

The COVID-19 pandemic caused school closures and social isolation, which created both learning and emotional challenges for adolescents. Schools worked hard to move classes online, but less attention was paid to whether students were cognitively and emotionally ready to learn effectively in a virtual environment. This study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period. Two groups of students participated in this study, with 1,316 high school students ( Mean age = 16.32, SD = 0.63) representing adolescents and 668 college students ( Mean age = 20.20, SD = 1.43) representing young adults. Structural equation modeling was conducted to explore the associations among online learning readiness, emotional competence, and online academic performance during COVID-19 after controlling for pre–COVID-19 academic performance. The results showed that, for high school students, both online learning readiness and emotional competence were positively associated with online academic performance during COVID-19. However, for college students, only online learning readiness showed a significant positive relationship with online academic performance during COVID-19. These results demonstrated that being ready to study online and having high emotional competence could make adolescents more resilient toward COVID-19–related challenges and help them learn more effectively online. This study also highlighted different patterns of associations among cognitive factors, emotional factors, and online academic performance during COVID-19 in adolescence and young adulthood. Developmental implications were also discussed.

COVID-19, as a public health crisis, stimulated a subsequent education crisis in which the existing achievement gap, learning loss, and dropout rate were exacerbated due to school closures (Sahu, 2020 ; United Nations, 2020 ). To prevent COVID-19 transmission, educational institutions worldwide made massive efforts to shift from in-person to online teaching (Basilaia & Kvavadze, 2020 ; Chen et al., 2020 ; Daniels et al., 2021 ; Subedi et al., 2020 ). However, little is known about whether students were cognitively and emotionally ready to learn effectively online at the time of transition.

COVID-19 created learning challenges caused by changes in educational platforms, especially for adolescents. Adolescence is a time when peer influences expand (Knoll et al., 2015 ; Knoll et al., 2016 ). With the dramatic changes in adolescents’ “social brain,” these students have a stronger desire for social interaction and are more sensitive to social isolation (Blakemore, 2008 ; Steinberg, 2005 ; Yurgelun-Todd, 2007 ). Social interactions with teachers, peers, and others are crucial elements in adolescents’ learning experiences (Perret-Clermont et al., 2004 ). Therefore, students struggle to be cognitively engaged in class without the motivation of in-person interactions with teachers and peers during online learning (Kim & Frick, 2011 ; Zembylas et al., 2008 ). Moreover, the new platform delivers information in an entirely different way within a totally different environment (i.e., school vs. home), which requires students to use technology and communicate effectively virtually while resisting distractions in the new environment (Aguilera-Hermida, 2020 ; Chen & Jang, 2010 ; Ferrer et al., 2020 ). In short, learning effectively online was extremely challenging during the pandemic.

In addition, COVID-19–related mental health difficulties, such as loss of relatives, social isolation, and heightened stress and anxiety (Hamza et al., 2020 ; Son et al., 2020 ; Wang et al., 2020 ), made students’ academic lives even more challenging (Grubic et al., 2020 ; Liang et al., 2020 ; Thakur, 2020 ; Zhai & Du, 2020 ; Zhao, 2021 ). As mentioned above, adolescence is a developmental stage characterized by a particularly sensitive “social brain” (Blakemore, 2008 ), and it is a critical period for emotional competence development (Booker & Dunsmore, 2017 ; Trentacosta & Fine, 2010 ). As such, any interpersonal and social-emotional suffering is magnified for adolescents when compared to individuals in other developmental stages. Students during this developmental stage need to have higher emotional competence to cope with emotional distress effectively, allowing them to be more resilient to the challenges of the COVID-19 pandemic and perform better academically (Baba, 2020 ; Bao, 2020 ). Therefore, this study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period.

COVID-19 and online learning readiness

Online learning readiness refers to students’ preparation to learn effectively in an online environment (Demir Kaymak & Horzum, 2013 ; Wei & Chou, 2020 ). Although whether students are ready for the “novice” online learning environment of the COVID-19 pandemic is an ongoing question, some preliminary findings provide insight into this question. Within higher education, according to Chung et al. ( 2020 ), students were generally ready for online learning in Malaysia. However, other researchers claimed that students’ learning readiness was lacking (Widodo et al., 2020 ). In high school settings, students were found to have inadequate digital skills for online learning in Delhi (Bhaumik & Priyadarshini, 2020 ). Conversely, Dwiyanti et al. ( 2020 ) reported that most junior high school students in Indonesia were ready and only needed a few improvements. Considering that each institution, country, and researcher may have different standards of “being ready” for online learning, a more meaningful question is this: How did online learning readiness influence students’ academic performance during the COVID-19 pandemic?

Online learning readiness and academic performance

Facilitating academic success is especially important for adolescents and young adults because academic performance has significant implications for future career development (Negru-Subtirica & Pop, 2016 ; Van der Aar et al., 2019 ). The current pandemic is lowering adolescents’ academic motivation (Aboagye et al., 2020 ), inducing learning loss (Kuhfield & Tarasawa, 2020 ; Turner et al., 2020 ), and ultimately causing lower academic performance (Kuhfeld et al., 2020 ). This phenomenon is partly due to a lack of readiness for online learning. According to the OECD’s Programme in International Student Assessment (PISA), most adolescents from diverse countries (i.e., 15-year-olds in the 79 education systems in the PISA database) were not ready to learn online (Reimers & Schleicher, 2020 ).

Online learning is not purely about having a place or a computer with which to study. More importantly, it requires specific skills and online learning self-efficacy (Smith, 2005 ). Many studies have recognized the importance of students’ motivation in the online learning environment (e.g., Chen & Jang, 2010 ; Khalilzadeh & Khodi, 2021 ). One challenge of online learning readiness research is that researchers have used different constructs, some of which overlap with self-directed learning and motivation (e.g., Cigdem & Ozturk, 2016 ; Pintrich, 2000 ; Zimmerman, 2008 ). Based on previous studies and in an effort to distinguish online learning readiness from self-directed learning and motivation, the current study focused on the three most-used factors in the online learning readiness literature: computer and Internet self-efficacy, learners’ self-control in online contexts, and online communication self-efficacy (Hung et al., 2010 ; Yu, 2018 ).

Studies have indicated that these three online learning readiness factors are associated with students’ online academic performance. Computer and Internet self-efficacy concerns students’ confidence with computer and Internet use (Hatlevik et al., 2018 ; Torkzadeh et al., 2006 ). Having confidence in using Microsoft Office software or conducting Internet research enables online problem-solving, lessens the stress caused by technology, and improves academic performance (Compeau & Higgins, 1995 ; Eastin & LaRose, 2000 ; Tsai & Lin, 2004 ). Learners’ self-control in online contexts refers to students’ ability to avoid distractions from social media (e.g., Facebook or Instagram) and video games and to focus on online courses and assignments (Teng et al., 2014 ; Wang & Beasley, 2002 ). Finally, online communication self-efficacy reflects students’ willingness and confidence in online interactions with instructors and peers to deepen understanding, which benefits their learning outcomes and learning satisfaction (Roper, 2007 ; Yilmaz, 2017 ). Having computer and Internet self-efficacy, self-control in online contexts, and online communication self-efficacy assists students with the transition to the online learning environment (Miao et al., 2020 ). Ultimately, these three factors all contribute to students’ online learning performance.

Overall, online learning readiness has been shown to positively correlate with college students’ academic performance in the online learning environment (Davies & Graff, 2005 ; Lee & Choi, 2013 ; Yu, 2018 ). Moreover, research results have been consistent across studies in diverse college samples (Bernard et al., 2004 ; Joosten & Cusatis, 2020 ). However, before the current pandemic, the majority of online learning readiness studies focused on higher education. More studies are needed to address the role of online learning readiness in high school students’ online academic performance and to determine how to support high school students in preparing for online learning, especially during the COVID-19 pandemic.

COVID-19 and emotional competence

Beyond online learning preparedness (e.g., computer skills or self-control in an online learning environment) for virtual learning during the COVID-19 pandemic, students also need emotional competence to prepare them for the hectic world. Emotional competence is defined as an individual’s ability to express, regulate, and understand emotions (Denham et al., 2015 ; Saarni, 1999 , 2000 ). Special attention needs to be paid to adolescents’ emotional competence during the COVID-19 pandemic for two major reasons. First, emotional competence, as a crucial factor in academic performance (Brackett et al., 2012 ; Oberle et al., 2014 ; Rhoades et al., 2011 ) and effective functioning in adulthood (Kotsou et al., 2011 ; Takšić, 2002 ), are developed through socialization during adolescence (Valiente et al., 2020 ). With the unavoidable social isolation caused by COVID-19, adolescents have been shown to be less aware and less accepting of their own emotions (Hurrell et al., 2017 ; Valiente et al., 2020 ) and to have a harder time regulating their emotions (Casey et al., 2019 ; Cole, 2014 ). Indeed, several early works on COVID-19’s immediate impacts reported an increase in low emotional competence-related mental health issues in adolescents and young adults (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ).

Second, there is an urgent need for adolescents to be emotionally competent to deal with the extra emotional distress caused by COVID-19, including the experience of illness, loss of relatives, and financial difficulties during the pandemic (Li et al., 2021 ; Pan, 2020 ; Wathelet et al., 2020 ) as well as feelings of anxiety, depression, and sadness (Imran et al., 2020 ). Having high emotional competence would help students control and regulate their grief, sadness, and stress to cope with the new online learning environment more effectively (Baba, 2020 ; Moroń & Biolik-Moroń, 2020 ).

Emotional competence and academic performance

High emotional competence could not only lessen mental health issues but could also contribute to academic performance in both adolescent and young adult populations (Brackett et al., 2012 ; Harley et al., 2019 ; Parker et al., 2004 ). Low emotional competence is related to increased mental health problems (e.g., Janssen et al., 2020 ; Orgilés et al., 2020 ; Smirni et al., 2020 ), which in turn interfere with academic performance (Dekker et al., 2020 ; Tembo et al., 2017 ). COVID-19 escalated this linkage because adolescents had a harder time regulating emotions due to social relationship changes (Akgül & Atalan Ergin, 2021 ; Mathews et al., 2016 ) and experienced higher levels of emotional distress caused by COVID-19-related issues (Magson et al., 2021 ).

According to recent research, students with a better ability to perceive and regulate emotions had higher online learning readiness levels and were more resistant to online distractions (Engin, 2017 ), so they were more likely to have better academic performance in an online learning setting (Artino Jr & Jones II, 2012 ; Kim & Pekrun, 2014 ). However, most emotional competence studies have been conducted in traditional face-to-face learning settings and focused on specific emotions, so it is necessary to test the role of emotional competence in online settings, especially during the current pandemic. Moreover, emotional competence plays different roles in adolescents’ and young adults’ lives (Hallam et al., 2014 ; Kotsou et al., 2011 ), but few studies have differentiated the roles that emotional competence play in academic performance between adolescence (high school students) and young adulthood (college students). Therefore, more research is needed to address the role that emotional competence plays during the COVID-19 pandemic from a developmental perspective.

The current study

Above all, online learning readiness and emotional competence are critical for understanding adolescents’ academic performance during COVID-19. Given the lack of research on high school students’ online learning readiness and students’ emotional competence in online settings, little is known about whether online learning readiness and emotional competence may influence students’ academic performance differently for high school students (adolescents) and college students (young adults). Therefore, this study aimed to (a) investigate how online learning readiness and emotional competence contribute to students’ academic performance in both high school and college students during COVID-19 and (b) explore whether the pattern of associations would be different in high school students and college students. As mentioned above, college students with better online learning readiness have been shown to have higher online academic performance (e.g., Tsai & Lin, 2004 ; Yilmaz, 2017 ), and in a traditional face-to-face setting, students with higher emotional competence have tended to have better academic performance (e.g., Brackett et al., 2012 ; Harley et al., 2019 ). In aim (a), this study proposed two hypotheses: Hypothesis 1 —Both high school and college students with a higher level of online learning readiness will have better online academic performance during the COVID-19 pandemic; Hypothesis 2 —Both high school and college students with better emotional competence will have higher online academic performance during the COVID-19 pandemic. Without enough evidence in the extant literature for us to make a specific prediction, aim (b) will be examined in an exploratory manner.

Participants and procedure

High school sample.

This study recruited 1,689 first-year students from a high school in northeast China with medium education quality. As recommended by Kline ( 2015 ), the minimum sample-size-to-parameters ratio would be 10:1. In the high school sample, the number of model parameters that required statistical estimates was 99. The sample-size-to-parameters ratio in our study was 17:1, meeting the requirement of above 10:1. A survey was set up on Wen Juan Xing (a Chinese survey engine similar to Qualtrics). The head teacher first sent out the consent form to students’ parents through WeChat. Parents signed the form electronically and returned it to the head teacher. After obtaining consent from parents or guardians, the head teacher sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing “careless cases” (i.e., the responses from participants who failed the attention check), the final sample consisted of 1,316 first-year high school students (39.1% male, 53.8% female, and 7.1% preferred not to say). We incorporated two attention checking items to avoid careless responses. For example, for this question, please select disagree. Participants who answered both attention checking questions correctly were included in this study. Participants’ ages ranged from 15 to 18 years old ( Mean = 16.32, SD = 0.63); 94.2% identified their race as Han (i.e., the majority in China), and 5.8% identified as minorities.

College student sample

A sample of 1,049 college students was recruited from a 4-year university in northeast China with medium education quality. In the college sample, the number of model parameters that required statistical estimates was 75. The sample-size-to-parameters ratio was 14:1, above the recommended 10:1 (Kline, 2015 ). The same survey on Wen Juan Xing was used to collect data. A university lecturer first sent out the consent form to students or students’ parents or guardians through WeChat (with forms sent to parents/guardians only for those students who were under 18). After receiving the signed consent forms, the university lecturer sent the survey link to students through WeChat during students’ free time. The survey data were collected over a 2-week period in July 2020. After removing careless cases (i.e., the responses from participants who failed the attention check), the final sample consisted of 668 college students (43.3% male, 51.8% female, and 4.9% preferred not to say). Participants’ ages ranged from 17 to 25 years old ( Mean = 20.20, SD = 1.43). Among them, 149 were freshmen, 207 were sophomores, 76 were juniors, and 236 were seniors; 89.2% identified their race as Han (i.e., the majority in China), and 10.8% identified as minorities.



All questionnaires originally in English (i.e., questionnaires on emotional competence and online learning readiness) were translated into Chinese through translation and back-translation procedures (Beaton et al., 2000 ). Specifically, one Chinese postdoctoral student fluent in English translated the scales to Chinese, and another Chinese university lecturer back-translated all scales to ensure translation accuracy. A bilingual US university faculty member checked both the translated and back-translated scales to further validate the translation. The whole survey included demographic information (e.g., gender, age, race) and questionnaires on emotional competence and online learning readiness.

Emotional competence

Emotional competence was measured by the Short Profile of Emotional Competence (S-PEC), which demonstrated high internal reliability in the original study ( D-G Rho = 0.85; Mikolajczak et al., 2014 ). The S-PEC included five parallel subfactors in both the intrapersonal (10 items) and interpersonal (10 items) dimensions. Each of the five subfactors was assessed by two items. These subfactors were identification (e.g., “When I am touched by something, I immediately know what I feel”), comprehension (e.g., “I do not always understand why I respond in the way I do”), expression (e.g., “I find it difficult to explain my feelings to others even if I want to”), regulation (e.g., “When I am angry, I find it easy to calm myself down”), and utilization (“If I wanted, I could easily make someone feel uneasy”). All items were rated on a scale from 1 = never to 5 = very often . In our study, two items in each subfactor were averaged to create a composite score; a higher value indicated better emotional competence in that specific subfactor. In our samples, both the reliability (Cronbach’s α = 0.71 in the high school sample and 0.76 in the college sample) and the constructive validity (high school sample: χ 2 (25) = 48.12, p = 0.004, CFI = 0.99, TLI = 0.98, RMSEA (90% CI) = 0.03 (0.02–0.04), SRMR = 0.02; college sample: χ 2 (29) = 59.62, p = 0.001, CFI = 0.98, TLI = 0.97, RMSEA (90% CI) = 0.04 (0.03–0.05), SRMR = 0.03) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further validate the psychometric properties of this translated instrument, we conducted item response theory analyses, like Alavi et al. ( 2021 ) and Khodi et al. ( 2021 ). Specifically, we applied the polytomous Rasch Rating Scale model (Andrich, 1978 ) to both the high school and college samples. Rasch measurement theory provides a clear and theoretically based framework that allows researchers to evaluate the degree to which the instrument adheres to invariant measurement (Martha et al., 2021 ; Wind et al., 2021 ; Wind & Guo, 2019 ). We used Winsteps software (Linacre, 2016 ) to obtain model-data fit statistics (i.e., infit and outfit MSE ) and the reliability of separation statistics ( Rel ) for students and items. On average, the values of model-data fit statistics were around 1 for both high school students ( M infit MSE = 1.01, SD = 0.73; M outfit MSE = 1.02, SD = 0.72) and college students ( M infit MSE = 1.02, SD = 0.88; M outfit MSE = 1.00, SD = 0.84), and for items, the infit and outfit MSE were also close to 1 (high school sample: M infit MSE = 1.03, SD = 0.28; M outfit MSE = 1.02, SD = 0.26; college sample: M infit MSE = 1.00, SD = 0.27; M outfit MSE = 1.00, SD = 0.26), indicating acceptable fit to the Rasch model. The reliability of the separation statistic for students (high school sample: Rel = 0.86; college sample: Rel = 0.88) suggests that the instrument effectively differentiated students with different levels of emotional competence. Similarly, the reliability of the separation statistic for items (high school sample: Rel = 1.00; college sample: Rel = 1.00) indicates that there were differences in difficulty to endorse each item. We also conducted differential item functioning (DIF) analysis to determine whether the item response differed between high school students and college students while controlling for an estimate of emotional competence. Several researchers (Draba, 1977 ; Wind & Guo, 2019 ; Wright et al., 1976 ) have recommended that absolute logit differences that exceed 0.5 suggest that DIF occurs between two groups. Our results show that the range of differences in Rasch calibrations were from -0.38 logits to 0.43 logits, which indicates that there were no substantively meaningful differences between high school students and college students. In summary, the emotional competence instrument demonstrated acceptable psychometric properties for measuring emotional competence among both high school and college students.

Online learning readiness

Items that directly targeted the online learning environment on the Online Learning Readiness Scale (OLRS; Hung et al., 2010 ) were employed to measure online learning readiness. Specifically, there were three items in each of the following three subscales: computer/Internet self-efficacy (e.g., “I feel confident in my knowledge and skills of how to manage software for online learning,” Cronbach’s α = 0.74), learner control in online contexts (e.g., “I can direct my own learning progress in online courses,” Cronbach’s α = 0.73), and online communication self-efficacy (e.g., “I feel confident in expressing myself [emotions and humor] through text,” Cronbach’s α = 0.87). All items were rated from 1 = strongly disagree to 5 = strongly agree . Three items on each of the subscales were averaged to create a composite score so that a higher value indicated higher levels of online learning readiness on that subscale. In our samples, both reliability (Cronbach’s α ranged from 0.72 to 0.73 in the high school sample and 0.75 to 0.82 in the college sample) and constructive validity (high school sample: χ 2 (19) = 100.04, p < 0.001, CFI = 0.98, TLI = 0.96, RMSEA (90% CI) = 0.06 (0.05–0.07), SRMR = 0.02; college sample: χ 2 (22) = 45.70, p = 0.002, CFI = 0.99, TLI = 0.99, RMSEA (90% CI) = 0.04 (0.02–0.06), SRMR = 0.02) of this translated measure were acceptable.

Apart from the confirmatory factor analysis, to further evaluate the psychometric properties of the translated OLRS, we also conducted Rasch analysis as we did for S-PEC. The results indicate that OLRS exhibited acceptable psychometric properties for measuring both high school and college students’ online learning readiness. Specifically, the average values of model-data fit statistics were around 1 for both groups (high school sample: M infit MSE = 1.00, SD = 0.98, M outfit MSE = 1.00, SD = 0.97; college sample: M infit MSE = 0.96, SD = 1.15; M outfit MSE = 0.97, SD = 1.17) and items (high school sample: M infit MSE = 1.00, SD = 0.19, M outfit MSE = 1.00, SD = 0.20; college sample: M infit MSE = 0.99, SD = 0.19, M outfit MSE = 0.97, SD = 0.20). The reliability of separation statistics for students (high school sample: Rel = 0.86; college sample: Rel = 0.87) and for items (high school sample: Rel = 0.99; college sample: Rel = 0.98) suggest that OLRS can effectively differentiate among individuals with different levels of online learning readiness. DIF analysis demonstrated that there were no substantively meaningful differences between high school students and college students (-0.41 ≤ logit difference ≤ 0.33).

Academic performance

After getting approval from their institutions, consent from students and their parents/guardians (for minor-aged students), we obtained students’ academic performance (indicated by final exam scores) from their teachers in both the high school and the college samples. In the high school sample, we collected students’ final exam scores on Chinese, math, and English—three major disciplines in the Chinese high school education system (the maximum possible score for each discipline was 150). In the college sample, we gathered students’ average final exam scores across all courses they had taken (the maximum possible score was 100). We collected participants’ scores at two time points (T1 and T2) for both samples. T1 was before the COVID-19 pandemic when traditional face-to-face teaching was used, and T2 was during the COVID-19 pandemic when online synchronous teaching was used. Students in both samples had similar online learning experiences. Specifically, the online synchronous teaching adopted Dingding (a Chinese meeting software application like Zoom), and Microsoft Office programs were used for assignments. WeChat (a Chinese messaging app) was utilized for teacher–teacher, teacher–student, student–student, and teacher–parent communication. For the high school sample, data were collected in December 2019 (T1) and July 2020 (T2); for the college sample, data were collected in January 2020 (T1) and June 2020 (T2). Students were assigned a four-digit research ID to confidentially link their final exam scores and the survey results.

Plan of analysis

Data analysis was conducted in Mplus version 8.4 (Muthén & Muthén, 2017 ). In both the high school and college samples, measurement models via confirmatory factor analysis (CFA) were first estimated on the latent constructs of emotional competence, online learning readiness, and academic performance (high school sample only), individually. Specifically, the latent variable of emotional competence was indicated by 10 composite scores—identification, comprehension, expression, regulation, and utilization in both intrapersonal and interpersonal domains. The latent variable of online learning readiness was indicated by three composite scores of computer/Internet self-efficacy, learner control in online contexts, and online communication self-efficacy.

In the high school sample, the latent variable of pre-COVID academic performance was indicated by students’ final exam scores on Chinese, English, and math at T1, and the latent variable of during-COVID academic performance was indicated by these three scores at T2. An overall measurement model including both the T1 and T2 latent constructs of academic performance was conducted after a CFA for each time point. In the college sample, because there was only a single score for each time point, that single score was used as a manifest variable for academic performance at T1 and T2. In each measurement model, correlations between residual variances were added one at a time according to modification indices (Sorbom, 1989 ).

Next, we used structural regression models to examine the association between emotional competence, online learning readiness, and students’ during-COVID academic performance while controlling for their pre-COVID academic performance and the demographic characteristics of age and gender. That is, the T2 academic performance variable was regressed on age, gender, T1 academic performance, emotional competence, and online learning readiness. All predictors were allowed to correlate with each other. This analysis was conducted separately in the high school and college samples.

Both measurement models and structural regression models were estimated using full information maximum likelihood estimation to minimize the bias caused by missingness (Widaman, 2006 ). Overall model fit acceptability was evaluated using the following criteria: the comparative fit index (CFI) value was greater than 0.95, the Tucker-Lewis index (TLI) was greater than 0.90, the root mean square error of approximation (RMSEA) was less than 0.06, and the standardized root mean square residual (SRMR) value was less than 0.08 (Hu & Bentler, 1999 ). Standardized path coefficients were reported in each model.

Last, group invariance tests were conducted across gender groups in both the high school and college samples to indicate whether the overall structural regression model was significantly different by gender. This was done by comparing two multiple group models that did not include gender as a control variable: in the first model, all regression paths were freely estimated across groups; in the second model, all regression paths were constrained to be the same across groups. Changes in the CFI (ΔCFI) were used as a preferred approach for model fit comparison, with ΔCFI equal to or greater than 0.01 indicating a significant change in model fit caused by path constraints (Cheung & Rensvold, 2002 ). This approach was more suitable than the Chi-square difference test for a large sample.

Table ​ Table1 1 includes descriptive statistics and correlation information for the variables used in the structural regression models. The lower panel shows correlations in the high school sample, and the upper panel depicts correlations in the college sample. Overall, variables were correlated in the expected directions in both samples. Moreover, the CFA models for emotional competence, online learning readiness, and academic performance across T1 and T2 all showed a good fit with the data (see Table ​ Table2 2 ).

Correlations, Means, and Standard Deviations

Note . Statistically significant correlations are bold and underlined ( p < .05). For gender: 1=male, 2=female.

The lower panel presents correlations in the high school sample and the upper panel presents correlations in the college sample.

EC=Emotional Competence, intra=intrapersonal dimension, inter=interpersonal dimension, id=identification, co=comprehension, re=regulation, ut=utilization; OL=Online Learning, eff=computer/internet self-efficacy, con=learner control in online contexts, com=online communication self-efficacy; T1 score=pre-COVID final exam score, T2 score=during-COVID final exam score; H.=high school sample, C.=college sample.

Due to space limit, high school T1 score and T2 score were composite scores (i.e. average score of Chinese, English, and Math at T1 and T2) in this correlation table (but they were latent variables in the formal analyses).

Model fit information for measurement models and structural regression models

Note . * p < .05, ** p < .01.

M1-M5=measurement model 1-5, S=structural regression model, EC=Emotional Competence, OL=Online Learning Readiness, T1 score=Pre-COVID Academic Performance, T2 score=During-COVID Academic Performance.

Model fit information of M2-M4 in high school sample and M2 in college sample were not available, since there were only 3 manifest variables loading on 1 latent variable in each model and these models were just identified

In the high school sample, the structural regression model had acceptable model fit, where χ 2 (153) = 669.25, p < 0.01; CFI = 0.94; TLI = 0.92; RMSEA = 0.05 (90%: 0.05–0.06); SRMR = 0.05. All regression paths are listed in Fig. ​ Fig.1 1 (a). Both emotional competence ( β = 0.06, p = .030) and online learning readiness ( β = 0.07, p = .006) were significantly associated with high school students’ during-COVID academic performance, even after accounting for the stability of their academic performance from the pre-COVID to during-COVID periods ( β = 0.78, p < .001) and controlling for the influence of age ( β = - 0.02, p = .489) and gender ( β = 0.07, p = .009).

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Object name is 12144_2022_2699_Fig1_HTML.jpg

The Associations of Emotional Competence, Online Learning Readiness, and Academic Performance. All predictors were correlated with each other. Residuals were allowed to correlated according to modification indices

In the college sample, the structural regression model had good model fit, where χ 2 (95) = 192.80, p < 0.01; CFI = 0.97; TLI = 0.96; RMSEA = 0.04 (90%: 0.03–0.05); SRMR = 0.04. All regression paths are listed in Fig. ​ Fig.1 1 (b). Only online learning readiness ( β = 0.15, p = .003) was significantly associated with college students’ during-COVID academic performance after accounting for the stability of their academic performance from the pre-COVID to during-COVID period ( β = 0.61, p < .001) and controlling for the influence of age ( β = 0.07, p = .061) and gender ( β = 0.08, p = .024). However, unlike the high school group, the association between emotional competence and during-COVID academic performance was not significant for college students ( β = - 0.02, p = .756).

Overall, the pattern of associations among variables was consistent across gender groups in both the high school and college samples, which was indicated by the insignificant change in the overall model fit (high school sample: ΔCFI = .000; college sample: ΔCFI = .002) between the model with constrained regression paths (i.e., constrained model) and the model with freely estimated regression paths (i.e., freely estimated model) across gender groups. This suggests that the association among emotional competence, online learning readiness, and during-COVID academic performance was representative of the whole sample (in the high school sample and college sample) regardless of a participant’s gender.

The present study was designed to evaluate how online learning readiness and emotional competence are related to students’ online academic performance during the COVID-19 pandemic. The results of structural regression models in both the high school and college samples generally supported our hypotheses. Consistent with Hypothesis 1, online learning readiness was associated with academic performance significantly for both high school students and college students (after controlling for their pre-COVID academic performance). However, there were some nuanced differences in the association between emotional competence and academic performance in the two samples. Partially consistent with Hypothesis 2, emotional competence was significantly associated with high school students’ academic performance, but such an association was not significant for college students. This finding also shed light on our second exploratory research question about the potentially different patterns of association among these constructs during adolescence (high school sample) and young adulthood (college sample). The association between online learning readiness and online academic performance was consistent across the two samples, but the association between emotional competence and online academic performance during COVID-19 was different.

The findings for online learning readiness were consistent with previous research (e.g., Cigdem & Ozturk, 2016 ; Horzum et al., 2015 ) and highlighted the vital role of online learning readiness in the high school population. Both high school students and college students who are more ready to learn online had better online learning academic performance. Specifically, high school and college students who have confidence in using Microsoft Office programs, managing software, and using the search engines (e.g., Google and Yahoo) were more likely to have higher academic performance (Tsai & Lin, 2004 ). Moreover, as in previous studies (Roper, 2007 ; Yilmaz, 2017 ), students who could direct their own learning online, avoid online distractions (e.g., instant messages or surfing the Internet), and communicate effectively with peers or instructors online demonstrated stronger academic performance during COVID-19.

All these findings are in line with classical developmental psychology theories, especially Bandura’s ( 1969 , 1977 ) interactive triangle of personal factors, personal behaviors, and environmental factors and Vygotsky’s ( 1978 ) social learning theory. A change in social and learning environment could influence students’ learning significantly, and how well students’ responses fit the environment are key factors of the learning outcome. Online learning and the pandemic are foreign for both high school and college students; the more ready students are, or the more quickly they can adjust to the new environment, the better their learning outcomes will be (Tu, 2002 ).

Developmental differences were identified in the associations between emotional competence and academic performance. The association between emotional competence and during-COVID-19 academic performance in the high school sample confirmed the findings from previous research that high emotional competence could contribute to academic performance (Brackett et al., 2012 ; Garner, 2010 ). Adolescents who could identify, comprehend, regulate, and utilize their own or others’ emotions performed better academically (Brackett et al., 2012 ; Durlak et al., 2011 ; Zins et al., 2007 ). Such findings are consistent with Pekrun’s ( 2000 , 2006 ) control-value theory of achievement emotion, which highlights the emotional arousal in academic settings elicited by academic achievement. Achievement emotion can influence cognitive, motivational, and regulatory processes associated with learning and achievement. Conversely, negative emotions consume energy that is essential for cognition and impair academic performance (Meinhardt & Pekrun, 2003 ). Therefore, adolescents who could better identify and regulate emotion achieved higher grades in the current study.

However, in the college sample, no association was identified between emotional competence and academic performance. This discrepancy in the association pattern between emotional competence and during-COVID-19 online academic performance is likely due to two factors: developmental differences and different measurements of academic performance. Developmentally, adolescents may have a harder time regulating emotions due to brain, body, and social relationship changes (Casey et al., 2019 ; Miller-Slough & Dunsmore, 2016 ), so emotional competence appears to be more critical for adolescents than young adults. The discrepancy might also be caused partially by the different measures of GPA (i.e., high school—Chinese, math, and English total grade; college—a single average score).

The current study has both theoretical and practical implications. The relatively large pooled sample sizes (15–25 years of age) enabled us to make more generalizable statistical inferences about both high school students (adolescents) and college students (young adults), at least in the Chinese student population. Theoretically, this study added to the limited literature on adolescents’ online learning readiness (Tsai & Lin, 2004 ) and replicated prior work in the college population to emphasize the important role online learning readiness plays in online academic performance during young adulthood (e.g., Hung et al., 2010 ; Rafique et al., 2021 ). Moreover, our findings extended previous research on the impact of emotional competence on psychological development outcomes (e.g., Kotsou et al., 2011 ; Valiente et al., 2020 ) to highlight its crucial role in online academic performance, especially for high school students.

Practically, this study informed both high schools and higher education institutions that preparing students to learn online is as essential as preparing the institution to operate online (Habibu et al., 2012 ; Littlejohn & Pegler, 2007 ). Being ready to transition to an online learning environment and having high emotional competence could make adolescents more resilient to COVID-19-related challenges, such as social isolation and learning loss (Shanahan et al., 2020 ). Educational institutions not only need to provide instructions on how to use Microsoft Office software and online searching techniques but should also provide learning strategies like how to avoid online distractions (e.g., social media and video games) and how to communicate effectively with teachers and peers online. Such guidance would be especially beneficial for students who think they are not ready for online learning. Moreover, students’ mental health issues need to be addressed by emotional competence-related interventions, especially for adolescents (Lau & Wu, 2012 ). Schools and universities should consider having interventions and training on emotional competence to promote students’ mental health and help them navigate the volatile, uncertain, complex, and ambiguous world (Hadar et al., 2020 ). Effective strategies of identifying, comprehending, regulating, and utilizing emotions should be offered via online instructions and activities, especially for high school students. Moreover, online counseling should be more accessible for adolescents (O’Connor, 2020 ; Wen et al., 2020 ).


This study has some limitations that should be considered when interpreting its results. First, although pre-COVID academic performance has been controlled for from a longitudinal perspective, the directionality of the association between online learning readiness, emotional competence, and online academic performance during the COVID-19 pandemic could not be deduced due to the cross-sectional nature of the current data. The different measures of grade point average across the sample may have contributed to different findings for the groups Second, self-reported data on emotional competence and online learning readiness unavoidably introduced bias into the measurements. Thirdly, this study did not account for demographic control variables such as socioeconomic status, which can be a key factor contributing to students’ access to computers and the Internet or other resources. Moreover, the data collection intervals were different for high school and college students, being 2 months less for the latter group.

Future directions

Future studies that include students in small towns and rural areas will enrich the generalizability of our findings because our samples were predominantly students from cities. Rural or suburban students would likely have less access to online resources or learning resources in general (Lai & Widmar, 2021 ). Moreover, longitudinal research is needed to infer the associational patterns of emotional competence and online learning readiness with academic performance, considering the enduring and emerging nature of emotional competence during adolescence (including young adulthood) and their potential nuanced implications for academic performance trajectory. Regardless, this is one of the first studies, to our knowledge, that simultaneously considered cognitive and emotional factors associated with online academic performance across different developmental stages in adolescence during the COVID-19 pandemic.

Availability of data and material


On behalf of all authors, the corresponding author states that there is no conflict of interest.

Not applicable.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Beijing Normal University and Dalian Neusoft University of Information. We are in compliance with the 1964 Declaration of Helsinki and its later addenda.

Informed consent was obtained from all individual participants included in the study. For participants under 18 years old, parent and guardian consent were obtained.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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  1. Three Minute Thesis

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  1. COVID-19’s impacts on the scope, effectiveness, and ...

    The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and ...

  2. The Impact of Online Learning on Student's Academic Performance

    The COVID-19 pandemic has not only changed the way people work but also how students conduct their studies. As national lockdowns are implemented, working and studying at home has become the norm, with some classes permanently moving to online-based learning (Davies, 2020). Even before the pandemic, online learning has been on the rise. The World

  3. Impact of online classes on the satisfaction and performance

    The aim of the study is to identify the factors affecting students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19 and to establish the relationship between these variables.

  4. Students’ online learning challenges during the pandemic and

    Abstract Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic.

  5. Online Education and the COVID-19 Outbreak: A Case Study of

    COVID-19 and Online Teaching. The emergence and unprecedented spread of the COVID-19 as a global pandemic has been posing substantial challenges to the practices of everyday life. There has been a surge of interest to explore the dynamics of online education across different contexts amid the COVID-19 pandemic [36–40].

  6. Academic performance under COVID-19: The role of online

    The COVID-19 pandemic caused school closures and social isolation, which created both learning and emotional challenges for adolescents. Schools worked hard to move classes online, but less attention was paid to whether students were cognitively and emotionally ready to learn effectively in a virtual environment.

  7. Effectiveness of Online Learning In Pandemic Covid-19

    1 Excerpt Impact, Effectiveness and Satisfaction of E-Learning among Undergraduate Students During Pandemic Covid-19